Exploring the role of the halo mass function for inferring astrophysical parameters during reionisation

Abstract

Abstract Detecting the 21-cm signal at z ≳ 6 will reveal insights into the properties of the first galaxies responsible for driving reionisation. To extract this information, we perform parameter inference with 3D simulations of the 21-cm signal embedded within a Bayesian inference pipeline. Presently, when performing inference, we must choose which sources of uncertainty to sample and which to hold fixed. Since the astrophysics of galaxies is much more uncertain than that of the underlying halo-mass function (HMF), we typically parameterise and model the former while fixing the latter. However, doing so may bias our inference of the galaxy properties. In this work, we explore the consequences of assuming an incorrect HMF and quantify the relative biases on our inferred astrophysical model parameters when considering the wrong HMF. We then relax this assumption by constructing a generalised five parameter HMF model and simultaneously recover it with our underlying astrophysical model. For this, we use 21CMFAST and perform Simulation-Based Inference using marginal neural ratio estimation to learn the likelihood-to-evidence ratio with Swyft. Using a mock 1000-hour observation of the 21-cm power spectrum from the forthcoming Square Kilometre Array, conservatively assuming foreground wedge avoidance, we find that assuming the incorrect HMF can bias the recovered astrophysical parameters by up to ∼3 − 4σ even when including independent information from observed luminosity functions. Using our generalised HMF model, although we recover our astrophysical parameters with a factor of ∼2 − 4 larger marginalised uncertainties, the constraints are unbiased, agnostic to the underlying HMF and therefore more conservative.